| /* |
| * Copyright (c) 2017-2022 Arm Limited. |
| * |
| * SPDX-License-Identifier: MIT |
| * |
| * Permission is hereby granted, free of charge, to any person obtaining a copy |
| * of this software and associated documentation files (the "Software"), to |
| * deal in the Software without restriction, including without limitation the |
| * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or |
| * sell copies of the Software, and to permit persons to whom the Software is |
| * furnished to do so, subject to the following conditions: |
| * |
| * The above copyright notice and this permission notice shall be included in all |
| * copies or substantial portions of the Software. |
| * |
| * THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| * FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| * OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| * SOFTWARE. |
| */ |
| #include "arm_compute/core/Helpers.h" |
| #include "arm_compute/core/Types.h" |
| #include "arm_compute/runtime/NEON/functions/NEDirectConvolutionLayer.h" |
| #include "arm_compute/runtime/Tensor.h" |
| #include "arm_compute/runtime/TensorAllocator.h" |
| #include "tests/NEON/Accessor.h" |
| #include "tests/PaddingCalculator.h" |
| #include "tests/datasets/ShapeDatasets.h" |
| #include "tests/framework/Asserts.h" |
| #include "tests/framework/Macros.h" |
| #include "tests/framework/datasets/Datasets.h" |
| #include "tests/validation/Validation.h" |
| #include "tests/validation/fixtures/DirectConvolutionLayerFixture.h" |
| |
| namespace arm_compute |
| { |
| namespace test |
| { |
| namespace validation |
| { |
| namespace |
| { |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| const RelativeTolerance<half_float::half> rel_tolerance_f16(half_float::half(0.2f)); /**< Relative tolerance value for FP16 types */ |
| const AbsoluteTolerance<float> abs_tolerance_f16(0.2f); /**< Absolute tolerance for FP16 types */ |
| constexpr float tolerance_num = 0.07f; /**< Tolerance number for the FP16 implementation */ |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| constexpr AbsoluteTolerance<float> tolerance_fp32(0.001f); /**< Tolerance for floating point tests */ |
| |
| /** Direct convolution data set.for FP32 */ |
| const auto data_pad_f32 = concat(concat(combine(framework::dataset::make("PadX", { 0, 1 }), |
| combine(framework::dataset::make("PadY", { 0, 1 }), |
| framework::dataset::make("KernelSize", 3))), |
| combine(framework::dataset::make("PadX", { 0, 2 }), |
| combine(framework::dataset::make("PadY", { 0, 2 }), |
| framework::dataset::make("KernelSize", 3)))), |
| combine(framework::dataset::make("PadX", { 0, 3 }), |
| combine(framework::dataset::make("PadY", { 0, 3 }), |
| framework::dataset::make("KernelSize", 5)))); |
| |
| /** Direct convolution data set.for FP16 */ |
| const auto data_pad_f16 = concat(combine(framework::dataset::make("PadX", { 0, 1 }), |
| combine(framework::dataset::make("PadY", { 0, 1 }), |
| framework::dataset::make("KernelSize", 3))), |
| combine(framework::dataset::make("PadX", { 0 }), |
| combine(framework::dataset::make("PadY", { 0 }), |
| framework::dataset::make("KernelSize", 1)))); |
| |
| const auto data_f32 = combine(datasets::SmallDirectConvolutionShapes(), |
| combine(framework::dataset::make("StrideX", { 1, 2, 3, 4 }), |
| combine(framework::dataset::make("StrideY", { 1, 2, 3, 4 }), |
| data_pad_f32))); |
| |
| const auto data_f16 = combine(datasets::SmallDirectConvolutionShapes(), |
| combine(framework::dataset::make("StrideX", { 1, 2, 3 }), |
| combine(framework::dataset::make("StrideY", { 1, 2, 3 }), |
| data_pad_f16))); |
| |
| const auto data_prec = combine(datasets::SmallDirectConvolutionShapes(), |
| combine(framework::dataset::make("StrideX", { 1 }), |
| combine(framework::dataset::make("StrideY", { 1 }), |
| combine(framework::dataset::make("PadX", { 1 }), |
| combine(framework::dataset::make("PadY", { 1 }), |
| framework::dataset::make("KernelSize", 3)))))); |
| |
| const auto data9x9 = combine(datasets::SmallDirectConvolutionShapes(), |
| combine(framework::dataset::make("StrideX", { 1, 2, 3 }), |
| combine(framework::dataset::make("StrideY", { 1, 2, 3 }), |
| combine(framework::dataset::make("PadX", { 0, 2 }), |
| combine(framework::dataset::make("PadY", { 0, 3 }), |
| framework::dataset::make("KernelSize", 9)))))); |
| |
| const auto data8x8 = combine(datasets::SmallDirectConvolutionShapes(), |
| combine(framework::dataset::make("StrideX", { 1, 2, 3 }), |
| combine(framework::dataset::make("StrideY", { 1, 2, 3 }), |
| combine(framework::dataset::make("PadX", { 0 }), |
| combine(framework::dataset::make("PadY", { 0 }), |
| framework::dataset::make("KernelSize", 8)))))); |
| |
| const auto data_f32_nightly = combine(data_f32, framework::dataset::make("NumKernels", { 1, 4, 5 })); |
| const auto data_f16_nightly = combine(data_f16, framework::dataset::make("NumKernels", { 1, 4, 5 })); |
| |
| const auto data_precommit = combine(data_prec, framework::dataset::make("NumKernels", { 1 })); |
| const auto data_precommit9x9 = combine(data9x9, framework::dataset::make("NumKernels", { 4 })); |
| const auto data_precommit8x8 = combine(data8x8, framework::dataset::make("NumKernels", { 4 })); |
| |
| /* The following tests is from real use-case that made DirectConvolution |
| * overflows in terms of its tensor indexing. This test case is using |
| * a separate tolerance due to the following reason. |
| * - It has shown that it requires generally larger absolute tolerance |
| * for large numbers or larger relative tolerance for small numbers. |
| * - With the first reason, since it is mainly testing index overflow, |
| * a value with a margin is used to avoid uninteded test failures |
| * during nightly. |
| */ |
| constexpr AbsoluteTolerance<float> usecase_tolerance_fp32(0.05f); |
| |
| const auto data_nightly_usecase = combine(framework::dataset::make("InputShape", { TensorShape{ 3U, 800U, 800U } }), |
| combine(framework::dataset::make("StrideX", { 1 }), |
| combine(framework::dataset::make("StrideY", { 1 }), |
| combine(framework::dataset::make("PadX", { 4 }), |
| combine(framework::dataset::make("PadY", { 4 }), |
| combine(framework::dataset::make("KernelSize", 9), |
| framework::dataset::make("NumKernels", { 16 }))))))); |
| |
| /** Activation function Dataset*/ |
| const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f) |
| }); |
| } // namespace |
| |
| TEST_SUITE(NEON) |
| TEST_SUITE(DirectConvolutionLayer) |
| |
| /** Check whether the configuration of a Direct Convolution layer with no |
| * bias leads to a successful execution. |
| */ |
| TEST_CASE(NoBias, framework::DatasetMode::PRECOMMIT) |
| { |
| const auto src_shape = TensorShape(27U, 13U, 2U); |
| const auto weights_shape = TensorShape(3U, 3U, 2U, 4U); |
| const auto bias_shape = TensorShape(4U); |
| const auto dst_shape = TensorShape(25U, 11U, 4U); |
| constexpr auto dt = DataType::F32; |
| |
| auto src = create_tensor<Tensor>(src_shape, dt); |
| auto weights = create_tensor<Tensor>(weights_shape, dt); |
| auto dst = create_tensor<Tensor>(dst_shape, dt); |
| |
| const auto conv_info = PadStrideInfo(1, 1, 0, 0); |
| |
| // Create Direct Convolution function |
| NEDirectConvolutionLayer conv{}; |
| conv.configure(&src, &weights, nullptr, &dst, conv_info); |
| |
| src.allocator()->allocate(); |
| weights.allocator()->allocate(); |
| dst.allocator()->allocate(); |
| |
| library->fill_tensor_value(Accessor(src), 1.f); |
| library->fill_tensor_value(Accessor(weights), 1.f); |
| |
| conv.run(); |
| |
| // Compute reference to compare |
| SimpleTensor<float> ref_src{ src_shape, dt }; |
| SimpleTensor<float> ref_weights{ weights_shape, dt }; |
| SimpleTensor<float> ref_bias{ bias_shape, dt }; |
| library->fill_tensor_value(ref_src, 1.f); |
| library->fill_tensor_value(ref_weights, 1.f); |
| // No bias |
| library->fill_tensor_value(ref_bias, 0.f); |
| auto ref_dst = reference::convolution_layer<float>(ref_src, ref_weights, ref_bias, dst_shape, conv_info); |
| |
| validate(Accessor(dst), ref_dst); |
| } |
| |
| // *INDENT-OFF* |
| // clang-format off |
| DATA_TEST_CASE(Validate, framework::DatasetMode::ALL, zip(zip(zip(zip(zip(zip( |
| framework::dataset::make("InputInfo", { TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching data type input/weights |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Mismatching input feature maps |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Unsupported kernel width |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Non-rectangular weights dimensions |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid weights dimensions |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid stride |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases size |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid biases dimensions |
| TensorInfo(TensorShape(27U, 13U, 2U), 1, DataType::F32), // Invalid output size |
| }), |
| framework::dataset::make("WeightsInfo",{ TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F16), |
| TensorInfo(TensorShape(3U, 3U, 3U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(9U, 9U, 2U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(5U, 3U, 2U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U, 3U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U, 3U, 2U, 4U), 1, DataType::F32), |
| })), |
| framework::dataset::make("BiasesInfo",{ TensorInfo(TensorShape(4U), 1, DataType::F32), |
| TensorInfo(TensorShape(4U), 1, DataType::F32), |
| TensorInfo(TensorShape(4U), 1, DataType::F32), |
| TensorInfo(TensorShape(4U), 1, DataType::F32), |
| TensorInfo(TensorShape(4U), 1, DataType::F32), |
| TensorInfo(TensorShape(4U), 1, DataType::F32), |
| TensorInfo(TensorShape(3U), 1, DataType::F32), |
| TensorInfo(TensorShape(4U, 2U), 1, DataType::F32), |
| TensorInfo(TensorShape(4U), 1, DataType::F32), |
| })), |
| framework::dataset::make("OutputInfo",{ TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(25U, 11U, 4U), 1, DataType::F32), |
| TensorInfo(TensorShape(26U, 11U, 4U), 1, DataType::F32), |
| })), |
| framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(3, 3, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| PadStrideInfo(1, 1, 0, 0), |
| })), |
| framework::dataset::make("ActivationInfo", |
| { |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(), |
| ActivationLayerInfo(), |
| ActivationLayerInfo(), |
| ActivationLayerInfo(), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), |
| })), |
| framework::dataset::make("Expected", { false, false, false, false, false, false, false, false, false })), |
| input_info, weights_info, biases_info, output_info, conv_info, act_info, expected) |
| { |
| bool is_valid = bool(NEDirectConvolutionLayer::validate(&input_info.clone()->set_is_resizable(false), &weights_info.clone()->set_is_resizable(false), &biases_info.clone()->set_is_resizable(false), &output_info.clone()->set_is_resizable(false), conv_info, act_info)); |
| ARM_COMPUTE_EXPECT(is_valid == expected, framework::LogLevel::ERRORS); |
| } |
| // clang-format on |
| // *INDENT-ON* |
| |
| DATA_TEST_CASE(NoPaddingNHWCKernel, framework::DatasetMode::ALL, combine(combine(combine(data_precommit, |
| framework::dataset::make("DataType", DataType::F32)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NHWC })), |
| |
| shape, stride_x, stride_y, pad_x, pad_y, kernel_size, num_kernels, data_type, act_info, data_layout) |
| { |
| TensorShape input_shape = TensorShape(shape); |
| TensorShape weights_shape(kernel_size, kernel_size, input_shape.z(), num_kernels); |
| const PadStrideInfo info(stride_x, stride_y, pad_x, pad_y, DimensionRoundingType::FLOOR); |
| |
| TensorInfo input_info = TensorInfo(input_shape, 1, data_type); |
| TensorInfo weights_info = TensorInfo(weights_shape, 1, data_type); |
| |
| TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, info); |
| |
| if(data_layout == DataLayout::NHWC) |
| { |
| permute(input_shape, PermutationVector(2U, 0U, 1U)); |
| permute(weights_shape, PermutationVector(2U, 0U, 1U)); |
| permute(output_shape, PermutationVector(2U, 0U, 1U)); |
| } |
| |
| // Create tensors |
| Tensor src = create_tensor<Tensor>(input_shape, data_type, 1, QuantizationInfo(), data_layout); |
| Tensor weights = create_tensor<Tensor>(weights_shape, data_type, 1, QuantizationInfo(), data_layout); |
| Tensor dst = create_tensor<Tensor>(output_shape, data_type, 1, QuantizationInfo(), data_layout); |
| |
| // Create and configure function |
| NEDirectConvolutionLayer conv; |
| conv.configure(&src, &weights, nullptr, &dst, info, act_info); |
| |
| validate(src.info()->padding(), PaddingSize(0, 0, 0, 0)); |
| validate(weights.info()->padding(), PaddingSize(0, 0, 0, 0)); |
| validate(dst.info()->padding(), PaddingSize(0, 0, 0, 0)); |
| } |
| |
| template <typename T> |
| using NEDirectConvolutionLayerFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T>; |
| template <typename T> |
| using NEDirectConvolutionLayerMixedDataLayoutFixture = DirectConvolutionValidationFixture<Tensor, Accessor, NEDirectConvolutionLayer, T, true>; |
| |
| TEST_SUITE(Float) |
| #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC |
| TEST_SUITE(FP16) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType", |
| DataType::F16)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", DataLayout::NCHW))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<half>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_f16_nightly, framework::dataset::make("DataType", DataType::F16)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", DataLayout::NCHW))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16); |
| } |
| TEST_SUITE_END() // FP16 |
| #endif /* __ARM_FEATURE_FP16_VECTOR_ARITHMETIC */ |
| |
| TEST_SUITE(FP32) |
| FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, framework::dataset::make("DataType", |
| DataType::F32)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_fp32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEDirectConvolutionLayerMixedDataLayoutFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit, |
| framework::dataset::make("DataType", DataType::F32)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_fp32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall8x8, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit8x8, framework::dataset::make("DataType", |
| DataType::F32)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_fp32); |
| } |
| |
| FIXTURE_DATA_TEST_CASE(RunSmall9x9, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::PRECOMMIT, combine(combine(combine(data_precommit9x9, framework::dataset::make("DataType", |
| DataType::F32)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_fp32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLarge, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_f32_nightly, framework::dataset::make("DataType", |
| DataType::F32)), |
| ActivationFunctionsDataset), |
| framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, tolerance_fp32); |
| } |
| FIXTURE_DATA_TEST_CASE(RunLargeUsecase, NEDirectConvolutionLayerFixture<float>, framework::DatasetMode::NIGHTLY, combine(combine(combine(data_nightly_usecase, framework::dataset::make("DataType", |
| DataType::F32)), |
| framework::dataset::make("ActivationInfo", { ActivationLayerInfo() })), |
| framework::dataset::make("DataLayout", { DataLayout::NHWC }))) |
| { |
| // Validate output |
| validate(Accessor(_target), _reference, usecase_tolerance_fp32); |
| } |
| TEST_SUITE_END() // FP32 |
| TEST_SUITE_END() // Float |
| TEST_SUITE_END() // DirectConvolutionLayer |
| TEST_SUITE_END() // Neon |
| } // namespace validation |
| } // namespace test |
| } // namespace arm_compute |
